A complete guide on how to build Image Search Engine

A complete guide on how to build Image Search Engine, available at $44.99, has an average rating of 3.8, with 19 lectures, based on 14 reviews, and has 79 subscribers.

You will learn about Learn how to build image search engine from scratch This course is ideal for individuals who are Beginner Python Developer curious about data science It is particularly useful for Beginner Python Developer curious about data science.

Enroll now: A complete guide on how to build Image Search Engine

Summary

Title: A complete guide on how to build Image Search Engine

Price: $44.99

Average Rating: 3.8

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to build image search engine from scratch

Who Should Attend

Beginner Python Developer curious about data science

Target Audiences

Beginner Python Developer curious about data science

Course Description

Learn to build image SEARCH  engine with using deep learning .  Deep learning is popular where a machine can be trained to search images based on patterns in the images.  Once trained, it can be used to search for similar images.

A lot of smart researchers have already spent lot of time building really good image classification networks like VGGNET, RESNET, Inception V3. The networks are variants of CNN. These networks have been trained on imagenet animal dataset. If your dataset requires a different type of image classification, you could just start with these networks and fine tune them on your smaller dataset. This saves significant time and resources. We are going to leverage VGG in this course.

Build a strong foundation in image search engines  with this tutorial for beginners.

Understanding fundamentals image search

Understanding fundamentals of deep learning , CNN and VGG

Benefits of VGG and Glove embeddings

Learn to use image and text embeddings

Understand approximate nearest neighbor algorithm

Use Spotify’s Annoy index  for faster retrieval

Learn how to apply VGG with real example of visual similarity search

Use Jupyter Notebook for step by step programming

Fine tune accuracy of model for performing text to image and image to text search

Build a real life web application for visual similarity search classification

A Powerful Skill at Your Fingertips  Learning the fundamentals image search  puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I’ll be covering topics like deep learning, Convolution and CNN   from scratch.

Jobs in image search area are plentiful, and being able to learn transfer learning will give you a strong edge. Image embedding is  state of art technology that can quickly help you achieve your goal.

Learning image search with VGG will help you become a computer vision developer which is in high demand.

Content and Overview  

This course teaches you on how to build image search engine using open source Python and Jupyter framework.  You will work along with me step by step to build following answers

Introduction to image search engine

Introduction to image embeddings and text embeddings

Build an jupyter notebook step by step using VGG

Build a real world web application to find cat vs dog

What am I going to get from this course?

Learn VGG and build image search classification engine from professional trainer from your own desk.

Over 10 lectures teaching you how to build image search engine

Suitable for beginner programmers and ideal for users who learn faster when shown.

Visual training method, offering users increased retention and accelerated learning.

Breaks even the most complex applications down into simplistic steps.

Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: About Author

Chapter 2: Set up

Lecture 1: Install Anaconda

Lecture 2: Install Python Packages

Chapter 3: World of Embeddings

Lecture 1: Word Embeddings

Lecture 2: Glove embeddings

Lecture 3: Fundamentals of Deep neural Network

Lecture 4: Convolutional Neural Network Architecture

Lecture 5: Imaged Embeddings using VGG

Chapter 4: Getting Data and Code

Lecture 1: Source Code

Lecture 2: Loading Training Data

Chapter 5: Indexing Image and text emebddings

Lecture 1: Steps for building search engine

Lecture 2: Indexing Images and Labels

Lecture 3: Fundamentals of Approximate Nearest Neighbor Search

Chapter 6: Building Search Engine

Lecture 1: Image to Image Search

Lecture 2: Building Image To Text Search Index

Lecture 3: Performing Image2Text and Text2Image search

Chapter 7: Web Application

Lecture 1: Building Web application

Lecture 2: Next Steps

Instructors

Evergreen Technologies
Software Mentor

Rating Distribution

1 stars: 0 votes

2 stars: 2 votes

3 stars: 2 votes

4 stars: 7 votes

5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!